Instructions to use bynn-intelligence-com/decisionnode-edge with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use bynn-intelligence-com/decisionnode-edge with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="bynn-intelligence-com/decisionnode-edge")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("bynn-intelligence-com/decisionnode-edge")
model = AutoModelForMultimodalLM.from_pretrained("bynn-intelligence-com/decisionnode-edge", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use bynn-intelligence-com/decisionnode-edge with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf bynn-intelligence-com/decisionnode-edge:Q8_0
# Run inference directly in the terminal:
llama cli -hf bynn-intelligence-com/decisionnode-edge:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf bynn-intelligence-com/decisionnode-edge:Q8_0
# Run inference directly in the terminal:
llama cli -hf bynn-intelligence-com/decisionnode-edge:Q8_0

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf bynn-intelligence-com/decisionnode-edge:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf bynn-intelligence-com/decisionnode-edge:Q8_0

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf bynn-intelligence-com/decisionnode-edge:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf bynn-intelligence-com/decisionnode-edge:Q8_0

Use Docker

docker model run hf.co/bynn-intelligence-com/decisionnode-edge:Q8_0

Llama new
LM Studio
Jan

vLLM

How to use bynn-intelligence-com/decisionnode-edge with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "bynn-intelligence-com/decisionnode-edge"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "bynn-intelligence-com/decisionnode-edge",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/bynn-intelligence-com/decisionnode-edge:Q8_0

SGLang

How to use bynn-intelligence-com/decisionnode-edge with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "bynn-intelligence-com/decisionnode-edge" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "bynn-intelligence-com/decisionnode-edge",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "bynn-intelligence-com/decisionnode-edge" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "bynn-intelligence-com/decisionnode-edge",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use bynn-intelligence-com/decisionnode-edge with Ollama:
```
ollama run hf.co/bynn-intelligence-com/decisionnode-edge:Q8_0
```
Unsloth Desktop
Docker Model Runner
How to use bynn-intelligence-com/decisionnode-edge with Docker Model Runner:
```
docker model run hf.co/bynn-intelligence-com/decisionnode-edge:Q8_0
```

Lemonade

How to use bynn-intelligence-com/decisionnode-edge with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull bynn-intelligence-com/decisionnode-edge:Q8_0

Run and chat with the model

lemonade run user.decisionnode-edge-Q8_0

List all available models

lemonade list

Atomic Chat

decisionnode-edge

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# Licensing

DecisionNode Edge and DecisionNode Edge Mini (the model weights and the `decisionnode_edge` runtime) are available under two licenses.

## Open source: AGPL-3.0

You may use, study, modify and share DecisionNode Edge under the GNU Affero General Public License, version 3 (`LICENSE`). This suits personal projects, education, research and open-source work.

If you distribute DecisionNode Edge or a work based on it, or let others use such a work over a network, the AGPL requires you to make the complete corresponding source of that work available to them under the same license.

## Commercial license

To use DecisionNode Edge in a commercial product or service without the obligations of the AGPL, for example inside closed-source software, in a device you sell or in a service you run for your customers, you need a commercial license from Bynn Intelligence Inc.

Write to licensing@bynn.com.

## The base models

DecisionNode Edge is built on Qwen3.5-2B-Base and DecisionNode Edge Mini on Qwen3.5-0.8B-Base. The Qwen team releases both under the Apache License, Version 2.0. That license is in `LICENSE-base`, and `NOTICE` lists the changes we made.